





import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import DecisionTransformerModel
     from transformers.models.decision_transformer.modeling_decision_transformer import (
         DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
     )



class 							_UpperCAmelCase       :

  """simple docstring"""


  def __init__(							self					:						str   ,    lowerCAmelCase_					:						Union[str, Any]   ,    lowerCAmelCase_					:						Optional[Any]=1_3   ,    lowerCAmelCase_					:						Optional[int]=7   ,    lowerCAmelCase_					:						List[str]=6   ,    lowerCAmelCase_					:						str=1_7   ,    lowerCAmelCase_					:						Dict=2_3   ,    lowerCAmelCase_					:						Dict=1_1   ,    lowerCAmelCase_					:						List[str]=True   ,    )				->		str:
     __lowerCAmelCase				      =      parent
     __lowerCAmelCase				      =      batch_size
     __lowerCAmelCase				      =      seq_length
     __lowerCAmelCase				      =      act_dim
     __lowerCAmelCase				      =      state_dim
     __lowerCAmelCase				      =      hidden_size
     __lowerCAmelCase				      =      max_length
     __lowerCAmelCase				      =      is_training






  def 							lowercase						(							self					:						Optional[int]	)				->		int:
     __lowerCAmelCase				      =      floats_tensor((self.batch_size, self.seq_length, self.state_dim)	)
     __lowerCAmelCase				      =      floats_tensor((self.batch_size, self.seq_length, self.act_dim)	)
     __lowerCAmelCase				      =      floats_tensor((self.batch_size, self.seq_length, 1)	)
     __lowerCAmelCase				      =      floats_tensor((self.batch_size, self.seq_length, 1)	)
     __lowerCAmelCase				      =      ids_tensor((self.batch_size, self.seq_length)   ,    vocab_size=1_0_0_0	)
     __lowerCAmelCase				      =      random_attention_mask((self.batch_size, self.seq_length)	)

     __lowerCAmelCase				      =      self.get_config()

     return (
         config,
         states,
         actions,
         rewards,
         returns_to_go,
         timesteps,
         attention_mask,
     )






  def 							lowercase						(							self					:						str	)				->		Union[str, Any]:
     return DecisionTransformerConfig(
         batch_size=self.batch_size   ,    seq_length=self.seq_length   ,    act_dim=self.act_dim   ,    state_dim=self.state_dim   ,    hidden_size=self.hidden_size   ,    max_length=self.max_length   ,    )






  def 							lowercase						(							self					:						Optional[int]   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						Union[str, Any]   ,    lowerCAmelCase_					:						Optional[Any]   ,    lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						Optional[Any]   ,    )				->		Dict:
     __lowerCAmelCase				      =      DecisionTransformerModel(config=lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     __lowerCAmelCase				      =      model(lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_	)

     self.parent.assertEqual(result.state_preds.shape   ,    states.shape	)
     self.parent.assertEqual(result.action_preds.shape   ,    actions.shape	)
     self.parent.assertEqual(result.return_preds.shape   ,    returns_to_go.shape	)
     self.parent.assertEqual(
         result.last_hidden_state.shape   ,    (self.batch_size, self.seq_length * 3, self.hidden_size)	)  # seq length *3 as there are 3 modelities: states, returns and actions






  def 							lowercase						(							self					:						int	)				->		Optional[Any]:
     __lowerCAmelCase				      =      self.prepare_config_and_inputs()
     (
         (
         __lowerCAmelCase
     )      ,      (
         __lowerCAmelCase
     )      ,      (
         __lowerCAmelCase
     )      ,      (
         __lowerCAmelCase
     )      ,      (
         __lowerCAmelCase
     )      ,      (
         __lowerCAmelCase
     )      ,      (
         __lowerCAmelCase
     )      ,      
     )				      =      config_and_inputs
     __lowerCAmelCase				      =      {
         'states': states,
         'actions': actions,
         'rewards': rewards,
         'returns_to_go': returns_to_go,
         'timesteps': timesteps,
         'attention_mask': attention_mask,
     }
     return config, inputs_dict





@require_torch
class 							_UpperCAmelCase       (       _UpperCamelCase						,       _UpperCamelCase						,       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  a_              =					(DecisionTransformerModel,) if is_torch_available() else ()
  a_              =					()
  a_              =					{"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

  # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
  a_              =					False

  # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
  a_              =					False
  a_              =					False
  a_              =					False
  a_              =					False
  a_              =					False
  a_              =					False
  a_              =					False
  a_              =					False
  a_              =					False






  def 							lowercase						(							self					:						int	)				->		Optional[Any]:
     __lowerCAmelCase				      =      DecisionTransformerModelTester(self	)
     __lowerCAmelCase				      =      ConfigTester(self   ,    config_class=lowerCAmelCase_   ,    hidden_size=3_7	)






  def 							lowercase						(							self					:						Optional[int]	)				->		Optional[int]:
     self.config_tester.run_common_tests()






  def 							lowercase						(							self					:						Optional[int]	)				->		int:
     __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase_	)






  @slow
  def 							lowercase						(							self					:						Union[str, Any]	)				->		Union[str, Any]:
     for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCAmelCase				      =      DecisionTransformerModel.from_pretrained(lowerCAmelCase_	)
        self.assertIsNotNone(lowerCAmelCase_	)






  def 							lowercase						(							self					:						Optional[Any]	)				->		str:
     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase				      =      model_class(lowerCAmelCase_	)
        __lowerCAmelCase				      =      inspect.signature(model.forward	)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __lowerCAmelCase				      =      [*signature.parameters.keys()]

        __lowerCAmelCase				      =      [
            'states',
            'actions',
            'rewards',
            'returns_to_go',
            'timesteps',
            'attention_mask',
        ]

        self.assertListEqual(arg_names[: len(lowerCAmelCase_	)]   ,    lowerCAmelCase_	)





@require_torch
class 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


  @slow
  def 							lowercase						(							self					:						List[str]	)				->		Any:
     __lowerCAmelCase				      =      2  # number of steps of autoregressive prediction we will perform
     __lowerCAmelCase				      =      1_0  # defined by the RL environment, may be normalized
     __lowerCAmelCase				      =      DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'	)
     __lowerCAmelCase				      =      model.to(lowerCAmelCase_	)
     __lowerCAmelCase				      =      model.config
     torch.manual_seed(0	)
     __lowerCAmelCase				      =      torch.randn(1   ,    1   ,    config.state_dim	).to(device=lowerCAmelCase_   ,    dtype=torch.floataa	)  # env.reset()

     __lowerCAmelCase				      =      torch.tensor(
         [[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]]   ,    device=lowerCAmelCase_	)

     __lowerCAmelCase				      =      torch.tensor(lowerCAmelCase_   ,    device=lowerCAmelCase_   ,    dtype=torch.floataa	).reshape(1   ,    1   ,    1	)
     __lowerCAmelCase				      =      state
     __lowerCAmelCase				      =      torch.zeros(1   ,    0   ,    config.act_dim   ,    device=lowerCAmelCase_   ,    dtype=torch.floataa	)
     __lowerCAmelCase				      =      torch.zeros(1   ,    0   ,    device=lowerCAmelCase_   ,    dtype=torch.floataa	)
     __lowerCAmelCase				      =      torch.tensor(0   ,    device=lowerCAmelCase_   ,    dtype=torch.long	).reshape(1   ,    1	)

     for step in range(lowerCAmelCase_	):
        __lowerCAmelCase				      =      torch.cat([actions, torch.zeros(1   ,    1   ,    config.act_dim   ,    device=lowerCAmelCase_	)]   ,    dim=1	)
        __lowerCAmelCase				      =      torch.cat([rewards, torch.zeros(1   ,    1   ,    device=lowerCAmelCase_	)]   ,    dim=1	)

        __lowerCAmelCase				      =      torch.ones(1   ,    states.shape[1]	).to(dtype=torch.long   ,    device=states.device	)

        with torch.no_grad():
           __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      model(
               states=lowerCAmelCase_   ,    actions=lowerCAmelCase_   ,    rewards=lowerCAmelCase_   ,    returns_to_go=lowerCAmelCase_   ,    timesteps=lowerCAmelCase_   ,    attention_mask=lowerCAmelCase_   ,    return_dict=lowerCAmelCase_   ,    )

        self.assertEqual(action_pred.shape   ,    actions.shape	)
        self.assertTrue(torch.allclose(action_pred[0, -1]   ,    expected_outputs[step]   ,    atol=1e-4	)	)
        __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      (  # env.step(action)
            torch.randn(1   ,    1   ,    config.state_dim	).to(device=lowerCAmelCase_   ,    dtype=torch.floataa	),
            1.0,
            False,
            {},
        )

        __lowerCAmelCase				      =      action_pred[0, -1]
        __lowerCAmelCase				      =      torch.cat([states, state]   ,    dim=1	)
        __lowerCAmelCase				      =      returns_to_go[0, -1] - reward
        __lowerCAmelCase				      =      torch.cat([returns_to_go, pred_return.reshape(1   ,    1   ,    1	)]   ,    dim=1	)
        __lowerCAmelCase				      =      torch.cat(
            [timesteps, torch.ones((1, 1)   ,    device=lowerCAmelCase_   ,    dtype=torch.long	) * (step + 1)]   ,    dim=1	)





from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = None   ,    snake_case = False   ,    snake_case = False   ,    snake_case = None   ,    snake_case = None   ,    **snake_case   ,    ):

						'''simple docstring'''


						super().__init__(
						    features=snake_case   ,    cache_dir=snake_case   ,    keep_in_memory=snake_case   ,    streaming=snake_case   ,    num_proc=snake_case   ,    **snake_case   ,    )
						UpperCamelCase__       =							Generator(
						    cache_dir=snake_case   ,    features=snake_case   ,    generator=snake_case   ,    gen_kwargs=snake_case   ,    **snake_case   ,    )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.streaming:
									UpperCamelCase__       =							self.builder.as_streaming_dataset(split="train"  )
						# Build regular (map-style) dataset
						else:
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None

									self.builder.download_and_prepare(
									    download_config=snake_case   ,    download_mode=snake_case   ,    verification_mode=snake_case   ,    base_path=snake_case   ,    num_proc=self.num_proc   ,    )
									UpperCamelCase__       =							self.builder.as_dataset(
									    split="train"   ,    verification_mode=snake_case   ,    in_memory=self.keep_in_memory  )
						return dataset


